Semantic Analysis in Natural Language Processing by Hemal Kithulagoda Voice Tech Podcast

Despite this structural change slightly impacting the semantic similarity with other translations, it did not significantly affect the semantic representation of the main body of The Analects when considering the overall data analysis. This study employs natural language processing (NLP) algorithms to analyze semantic similarities among five English translations of The Analects. To achieve this, a corpus is constructed from these translations, and three algorithms—Word2Vec, GloVe, and BERT—are applied to assess the semantic congruence of corresponding sentences among the different translations. Analysis reveals that core concepts, and personal names substantially shape the semantic portrayal in the translations. In conclusion, this study presents critical findings and provides insightful recommendations to enhance readers’ comprehension and to improve the translation accuracy of The Analects for all translators. A language can be defined as a set of rules or set of symbols where symbols are combined and used for conveying information or broadcasting the information.

Such nuances run the risk of being overlooked when attempting to communicate the semantics and context of the original text. The data displayed in Table 5 and Attachment 3 underscore significant discrepancies in semantic similarity (values ≤ 80%) among specific sentence pairs across the five translations, with a particular emphasis on variances in word choice. As mentioned earlier, the factors contributing to these differences can be multi-faceted and are worth exploring further. Conversely, the outcomes of semantic similarity calculations falling below 80% constitute 1,973 sentence pairs, approximating 22% of the aggregate number of sentence pairs. Although this subset of sentence pairs represents a relatively minor proportion, it holds pivotal significance in impacting semantic representation amongst the varied translations, unveiling considerable semantic variances therein. To delve deeper into these disparities and their foundational causes, a more comprehensive and meticulous analysis is slated for the subsequent sections.

[47] In order to observe the word arrangement in forward and backward direction, bi-directional LSTM is explored by researchers [59]. In case of machine translation, encoder-decoder architecture is used where dimensionality of input and output vector is not known. Neural networks can be used to anticipate a state that has not yet been seen, such as future states for which predictors exist whereas HMM predicts hidden states. The analysis of sentence pairs exhibiting low similarity underscores the significant influence of core conceptual words and personal names on the text’s semantic representation. The complexity inherent in core conceptual words and personal names can present challenges for readers. To bolster readers’ comprehension of The Analects, this study recommends an in-depth examination of both core conceptual terms and the system of personal names in ancient China.

Rather, we think about a theme (or topic) and then chose words such that we can express our thoughts to others in a more meaningful way. This article does not contain any studies with human participants performed by any of the authors. In conclusion, we eagerly anticipate the introduction and evaluation of state-of-the-art NLP tools more prominently in existing and new real-world clinical use cases in the near future.

You can foun additiona information about ai customer service and artificial intelligence and NLP. This study further subdivided these segments using punctuation marks, such as periods (.), question marks (?), and semicolons (;). However, it is crucial to note that these subdivisions were not exclusively reliant on punctuation marks. Instead, this study followed the principle of dividing the text into lines to make sure that each segment fully expresses the original meaning. Finally, each translated English text was aligned with its corresponding original text. For instance, Raghavan et al. [71] created a model to distinguish time-bins based on the relative temporal distance of a medical event from an admission date (way before admission, before admission, on admission, after admission, after discharge). The model was evaluated on a corpus of a variety of note types from Methicillin-Resistant S. Aureus (MRSA) cases, resulting in 89% precision and 79% recall using CRF and gold standard features.

In finance, NLP can be paired with machine learning to generate financial reports based on invoices, statements and other documents. Financial analysts can also employ natural language processing to predict stock market trends by analyzing news articles, social media posts and other online sources for market sentiments. Speech recognition, for example, has gotten very good and works almost flawlessly, but we still lack this kind of proficiency in natural language understanding. Your phone basically understands what you have said, but often can’t do anything with it because it doesn’t understand the meaning behind it. Also, some of the technologies out there only make you think they understand the meaning of a text.

BI-CARU Feature Extraction for Semantic Analysis

If that would be the case then the admins could easily view the personal banking information of customers with is not correct. Overload of information is the real thing in this digital age, and already our reach and access to knowledge and information exceeds our capacity to understand it. This trend is not slowing down, so an ability to summarize the data while keeping the meaning intact is highly required.

An approach based on keywords or statistics or even pure machine learning may be using a matching or frequency technique for clues as to what the text is “about.” But, because they don’t understand the deeper relationships within the text, these methods are limited. Semantic analysis is the process of understanding the meaning and interpretation of words, signs and sentence structure. I say this partly because semantic analysis is one of the toughest parts of natural language processing and it’s not fully solved yet. Understanding human language is considered a difficult task due to its complexity. For example, there are an infinite number of different ways to arrange words in a sentence.

In that case, it becomes an example of a homonym, as the meanings are unrelated to each other. It represents the relationship between a generic term and instances of that generic term. Here the generic term is known as hypernym and semantic analysis in natural language processing its instances are called hyponyms. In Meaning Representation, we employ these basic units to represent textual information. Semantic analysis, on the other hand, is crucial to achieving a high level of accuracy when analyzing text.

The NLP Problem Solved by Semantic Analysis

It then identifies the textual elements and assigns them to their logical and grammatical roles. Finally, it analyzes the surrounding text and text structure to accurately determine the proper meaning of the words in context. What we do in co-reference resolution is, finding which phrases refer to which entities. There are also words that such as ‘that’, ‘this’, ‘it’ which may or may not refer to an entity. We should identify whether they refer to an entity or not in a certain document. There have also been huge advancements in machine translation through the rise of recurrent neural networks, about which I also wrote a blog post.

Most proficient translators typically include detailed explanations of these core concepts and personal names either in the introductory or supplementary sections of their translations. If feasible, readers should consult multiple translations for cross-reference, especially when interpreting key conceptual terms and names. However, given the abundance of online resources, sourcing accurate and relevant information is convenient. Readers can refer to online resources like Wikipedia or academic databases such as the Web of Science.

But still there is a long way for this.BI will also make it easier to access as GUI is not needed. Because nowadays the queries are made by text or voice command on smartphones.one of the most common examples is Google might tell you today what tomorrow’s weather will be. But soon enough, we will be able to ask our personal data chatbot about customer sentiment today, and how we feel about their brand next week; all while walking down the street.

What Is Semantic Analysis?

While this process may be time-consuming, it is an essential step towards improving comprehension of The Analects. From readers cognitive enhancement perspective, this approach can significantly improve readers’ understanding and reading fluency, thus enhancing reading efficiency. Powered by machine learning algorithms and natural language processing, semantic analysis systems can understand the context of natural language, detect emotions and sarcasm, and extract valuable information from unstructured data, achieving human-level accuracy. A challenging issue related to concept detection and classification is coreference resolution, e.g. correctly identifying that it refers to heart attack in the example “She suffered from a heart attack two years ago. It was severe.” NLP approaches applied on the 2011 i2b2 challenge corpus included using external knowledge sources and document structure features to augment machine learning or rule-based approaches [57]. For instance, the MCORES system employs a rich feature set with a decision tree algorithm, outperforming unweighted average F1 results compared to existing open-domain systems on the semantic types Test (84%), Persons (84%), Problems (85%) and Treatments (89%) [58].

In contrast, sentences garnering high similarity via the Word2Vec algorithm typically correspond with elevated scores when evaluated by the GloVe and BERT algorithms. Rationalist approach or symbolic approach assumes that a crucial part of the knowledge in the human mind is not derived by the senses but is firm in advance, probably by genetic inheritance. It was believed that machines can be made to function like the human brain by giving some fundamental knowledge and reasoning mechanism linguistics knowledge is directly encoded in rule or other forms of representation. Statistical and machine learning entail evolution of algorithms that allow a program to infer patterns. An iterative process is used to characterize a given algorithm’s underlying algorithm that is optimized by a numerical measure that characterizes numerical parameters and learning phase. Machine-learning models can be predominantly categorized as either generative or discriminative.

It’s a good way to get started (like logistic or linear regression in data science), but it isn’t cutting edge and it is possible to do it way better. Now, imagine all the English words in the vocabulary with all their different fixations at the end of them. To store them all would require a huge database containing many words that actually have the same meaning. Popular algorithms for stemming include the Porter stemming algorithm from 1979, which still works well. The main difference between them is that in polysemy, the meanings of the words are related but in homonymy, the meanings of the words are not related. For example, if we talk about the same word “Bank”, we can write the meaning ‘a financial institution’ or ‘a river bank’.

Named entity recognition (NER) is a technique to recognize and separate the named entities and group them under predefined classes. But in the era of the Internet, where people use slang not the traditional or standard English which cannot be processed by standard natural language processing tools. Ritter (2011) [111] proposed the classification of named entities in tweets because standard NLP tools did not perform well on tweets. The goal of NLP is to accommodate one or more specialties of an algorithm or system.

NLP can help identify benefits to patients, interactions of these therapies with other medical treatments, and potential unknown effects when using non-traditional therapies for disease treatment and management e.g., herbal medicines. The objective of this section is to discuss evaluation metrics used to evaluate the model’s performance and involved challenges. The objective of this section is to present the various datasets used in NLP and some state-of-the-art models in NLP. There is a system called MITA (Metlife’s Intelligent Text Analyzer) (Glasgow et al. (1998) [48]) that extracts information from life insurance applications.

In the form of chatbots, natural language processing can take some of the weight off customer service teams, promptly responding to online queries and redirecting customers when needed. NLP can also analyze customer surveys and feedback, allowing teams to gather timely intel on how customers feel about a brand and steps they can take to improve customer sentiment. Let’s look at some of the most popular techniques used in natural language processing. Note how some of them are closely intertwined and only serve as subtasks for solving larger problems. Syntactic analysis, also referred to as syntax analysis or parsing, is the process of analyzing natural language with the rules of a formal grammar. Grammatical rules are applied to categories and groups of words, not individual words.

The framework requires additional refinement and evaluation to determine its relevance and applicability across a broad audience including underserved settings. Event discovery in social media feeds (Benson et al.,2011) [13], using a graphical model to analyze any social media feeds to determine whether it contains the name of a person or name of a venue, place, time etc. Parsing implies pulling out a certain set of words from a text, based on predefined rules. For example, we want to find out the names of all locations mentioned in a newspaper. Semantic analysis would be an overkill for such an application and syntactic analysis does the job just fine.

Muller et al. [90] used the BERT model to analyze the tweets on covid-19 content. The use of the BERT model in the legal domain was explored by Chalkidis et al. [20]. Earlier machine learning techniques such as Naïve Bayes, HMM etc. were majorly used for NLP but by the end of 2010, neural networks transformed and enhanced NLP tasks by learning multilevel features. Major use of neural networks in NLP is observed for word embedding where words are represented in the form of vectors. Initially focus was on feedforward [49] and CNN (convolutional neural network) architecture [69] but later researchers adopted recurrent neural networks to capture the context of a word with respect to surrounding words of a sentence. LSTM (Long Short-Term Memory), a variant of RNN, is used in various tasks such as word prediction, and sentence topic prediction.

Emphasized Customer-centric Strategy

Datasets used in NLP and various approaches are presented in Section 4, and Section 5 is written on evaluation metrics and challenges involved in NLP. Once these issues are addressed, semantic analysis can be used to extract concepts that contribute to our understanding of patient longitudinal care. For example, lexical and conceptual semantics can be applied to encode morphological aspects of words and syntactic aspects of phrases to represent the meaning of words in texts.

In early 1980s computational grammar theory became a very active area of research linked with logics for meaning and knowledge’s ability to deal with the user’s beliefs and intentions and with functions like emphasis and themes. By knowing the structure of sentences, we can start trying to understand the meaning of sentences. We start off with the meaning of words being vectors but we can also do this with whole phrases and sentences, where the meaning is also represented as vectors.

It is also essential for automated processing and question-answer systems like chatbots. However, many organizations struggle to capitalize on it because of their inability to analyze unstructured data. This challenge is a frequent roadblock for artificial intelligence (AI) initiatives that tackle language-intensive processes. The 18th edition of SemEval features 10 TASKS on a range of topics, including tasks on idiomaticy detection and embedding, sarcasm detection, multilingual news similarity, and linking mathematical symbols to their descriptions.

For example, “cows flow supremely” is grammatically valid (subject — verb — adverb) but it doesn’t make any sense. With the help of meaning representation, unambiguous, canonical forms can be represented at the lexical level. The most important task of semantic analysis is to get the proper meaning of the sentence. For example, analyze the sentence “Ram is great.” In this sentence, the speaker is talking either about Lord Ram or about a person whose name is Ram. That is why the job, to get the proper meaning of the sentence, of semantic analyzer is important.

Today, some hospitals have in-house solutions or legacy health record systems for which NLP algorithms are not easily applied. However, when applicable, NLP could play an important role in reaching the goals of better clinical and population health outcomes by the improved use of the textual content contained in EHR systems. Since the number of labels in most classification problems is fixed, it is easy to determine the score for each class and, as a result, the loss from the ground truth. In image generation problems, the output resolution and ground truth are both fixed. As a result, we can calculate the loss at the pixel level using ground truth. But in NLP, though output format is predetermined in the case of NLP, dimensions cannot be specified.

Noun phrases are one or more words that contain a noun and maybe some descriptors, verbs or adverbs. Below is a parse tree for the sentence “The thief robbed the apartment.” Included is a description of the three different information types conveyed by the sentence. Tutorials Point is a leading Ed Tech company striving to provide the best learning material on technical and non-technical subjects. It may be defined as the words having same spelling or same form but having different and unrelated meaning.

To maintain consistency in the similarity calculations within the parallel corpus, this study used “None” to represent untranslated sections, ensuring that these omissions did not impact our computational analysis. The analysis encompassed a total of 136,171 English words and 890 lines across all five translations. Similarly, the European Commission emphasizes the importance of eHealth innovations for improved healthcare in its Action Plan [106]. Such initiatives are of great relevance to the clinical NLP community and could be a catalyst for bridging health care policy and practice.

Fan et al. [34] adapted the Penn Treebank II guidelines [35] for annotating clinical sentences from the 2010 i2B2/VA challenge notes with high inter-annotator agreement (93% F1). This adaptation resulted in the discovery of clinical-specific linguistic features. This new knowledge was used to train the general-purpose Stanford statistical parser, resulting in higher accuracy than models trained solely on general or clinical sentences (81%). A consistent barrier to progress in clinical NLP is data access, primarily restricted by privacy concerns. De-identification methods are employed to ensure an individual’s anonymity, most commonly by removing, replacing, or masking Protected Health Information (PHI) in clinical text, such as names and geographical locations. Once a document collection is de-identified, it can be more easily distributed for research purposes.

• The objective of this section is to discuss the Natural Language Understanding (Linguistic) (NLU) and the Natural Language Generation (NLG).
• Finally, each translated English text was aligned with its corresponding original text.
• With the help of semantic analysis, machine learning tools can recognize a ticket either as a “Payment issue” or a“Shipping problem”.
• Similar to PCA, SVD also combines columns of the original matrix linearly to arrive at the U matrix.
• In other words, it shows how to put together entities, concepts, relations, and predicates to describe a situation.

This strategy enables the translator to maintain consistency with the original text while providing additional information about the meanings and backgrounds. This approach ensures simplicity and naturalness in expression, mirrors the original text as closely as possible, and maximizes comprehension and contextual impact with minimal cognitive effort. Among the five translations, only a select number of sentences from Slingerland and Watson consistently retain identical sentence structure and word choices, as in Table 4. The three embedding models used to evaluate semantic similarity resulted in a 100% match for sentences NO. 461, 590, and 616. In other high-similarity sentence pairs, the choice of words is almost identical, with only minor discrepancies. However, as the semantic similarity between sentence pairs decreases, discrepancies in word selection and phraseology become more pronounced.

We can do semantic analysis automatically works with the help of machine learning algorithms by feeding semantically enhanced machine learning algorithms with samples of text data, we can train machines to make accurate predictions based on their past results. This analysis gives the power to computers to understand and interpret sentences, paragraphs, or whole documents, by analyzing their grammatical structure, and identifying the relationships between individual words of the sentence in a particular context. While, as humans, it is pretty simple for us to understand the meaning of textual information, it is not so in the case of machines. Thus, machines tend to represent the text in specific formats in order to interpret its meaning.

Our proposed work utilizes Term Frequency-based Inverse Document Frequency model and Glove algorithm-based word embeddings vector for determining the semantic similarity among the terms in the textual contents. Lemmatizer is utilized to reduce the terms to the most possible smallest lemmas. The outcomes demonstrate that the proposed methodology is more prominent than the TF-idf score in ranking the terms with respect to the search query terms. The Pearson correlation coefficient achieved for the semantic similarity model is 0.875.

The metric of NLP assess on an algorithmic system allows for the integration of language understanding and language generation. Rospocher et al. [112] purposed a novel modular system for cross-lingual event extraction for English, Dutch, and Italian Texts by using different pipelines for different languages. The pipeline integrates modules for basic NLP processing as well as more advanced tasks such as cross-lingual named entity linking, semantic role labeling and time normalization. Thus, the cross-lingual framework allows for the interpretation of events, participants, locations, and time, as well as the relations between them. Output of these individual pipelines is intended to be used as input for a system that obtains event centric knowledge graphs.